In the context of real-world mobile robots, state estimation is the problem of estimating the state of a robot. Typically, states such as the exact location of a robot and the exact locations of obstacles in a robot's environment are not directly observable. However, such states can be inferred from sensor measurements. A robot can rely on sensor measurements to infer its state and the state of its environment. Unfortunately, sensor measurements can be noisy and the amount of noise can vary with state. Various methods of estimating and iteratively adapting measurement noise over time can be performed. However, those methods do not assume that measurement noise is stochastic and they do not estimate sensor measurement bias and noise based on state. It would be preferable to estimate noise and bias based on state of the robot.
The drawings illustrate only example embodiments and are therefore not to be considered limiting of the scope described herein, as other equally effective embodiments are within the scope and spirit of this disclosure. The elements and features shown in the drawings are not necessarily drawn to scale, emphasis instead being placed upon clearly illustrating the principles of the embodiments. Additionally, certain dimensions may be exaggerated to help visually convey certain principles. In the drawings, similar reference numerals between figures designate like or corresponding, but not necessarily the same, elements.